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modular camera calibration system mccs  (Autodesk Inc)

 
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    Structured Review

    Autodesk Inc modular camera calibration system mccs
    Mobile platform for the evaluation and development of CLIAs. a Workflow of the development and evaluation platform, including the selection of an image sensor, its acquisition conditions, measuring standard curves, and optimization studies. b Schematic illustration of the Mobile Camera Characterization System <t>(MCCS)</t> for the in silico sensor and signal processing evaluation. c Schematic illustration of the Mobile Reader (MR) for CLIA signal quantification. d Mobile app to control the MCCS and MR. e Quantification control and analysis for a biomarker standard curve targeting Renin. Analyte concentrations, the resulting raw image, and control of well detection accuracy (yellow circle). A squared region of interest (ROI, yellow box) and background (BG) indicate some of the areas assessed for signal and background quantification, respectively. f Box plot of raw pixel values from ROI as a function of the biomarker concentration analyzed by primary image channel (red, green, blue) with pairwise statistical significance test. The box represents the inter-quartile range (IQR), with the median (hereafter termed signal) shown as a horizontal line. Whiskers extend to the most extreme data points within 1.5 times the IQR. n = 3721 pixels.
    Modular Camera Calibration System Mccs, supplied by Autodesk Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/modular camera calibration system mccs/product/Autodesk Inc
    Average 86 stars, based on 1 article reviews
    modular camera calibration system mccs - by Bioz Stars, 2026-05
    86/100 stars

    Images

    1) Product Images from "CLIA MDK: A Modular Smartphone Platform Matching Plate Reader Performance for Chemiluminescent Immunoassay Development"

    Article Title: CLIA MDK: A Modular Smartphone Platform Matching Plate Reader Performance for Chemiluminescent Immunoassay Development

    Journal: medRxiv

    doi: 10.64898/2026.03.26.26348440

    Mobile platform for the evaluation and development of CLIAs. a Workflow of the development and evaluation platform, including the selection of an image sensor, its acquisition conditions, measuring standard curves, and optimization studies. b Schematic illustration of the Mobile Camera Characterization System (MCCS) for the in silico sensor and signal processing evaluation. c Schematic illustration of the Mobile Reader (MR) for CLIA signal quantification. d Mobile app to control the MCCS and MR. e Quantification control and analysis for a biomarker standard curve targeting Renin. Analyte concentrations, the resulting raw image, and control of well detection accuracy (yellow circle). A squared region of interest (ROI, yellow box) and background (BG) indicate some of the areas assessed for signal and background quantification, respectively. f Box plot of raw pixel values from ROI as a function of the biomarker concentration analyzed by primary image channel (red, green, blue) with pairwise statistical significance test. The box represents the inter-quartile range (IQR), with the median (hereafter termed signal) shown as a horizontal line. Whiskers extend to the most extreme data points within 1.5 times the IQR. n = 3721 pixels.
    Figure Legend Snippet: Mobile platform for the evaluation and development of CLIAs. a Workflow of the development and evaluation platform, including the selection of an image sensor, its acquisition conditions, measuring standard curves, and optimization studies. b Schematic illustration of the Mobile Camera Characterization System (MCCS) for the in silico sensor and signal processing evaluation. c Schematic illustration of the Mobile Reader (MR) for CLIA signal quantification. d Mobile app to control the MCCS and MR. e Quantification control and analysis for a biomarker standard curve targeting Renin. Analyte concentrations, the resulting raw image, and control of well detection accuracy (yellow circle). A squared region of interest (ROI, yellow box) and background (BG) indicate some of the areas assessed for signal and background quantification, respectively. f Box plot of raw pixel values from ROI as a function of the biomarker concentration analyzed by primary image channel (red, green, blue) with pairwise statistical significance test. The box represents the inter-quartile range (IQR), with the median (hereafter termed signal) shown as a horizontal line. Whiskers extend to the most extreme data points within 1.5 times the IQR. n = 3721 pixels.

    Techniques Used: Selection, In Silico, Control, Biomarker Discovery, Concentration Assay

    Camera Sensor Evaluation with MCCS. a Workflow using the MCCS to test configurations for CLIA testing. b Optical power limit for different cameras measured with a blue LED and varying numbers of neutral density filters (OD). Xiaomi 13 Pro (X13P), Huawei P60 Pro (HP60P), Huawei P20 (HP20), Fairphone 5 (F5), Nokia G22 (NG22), Arducam 3MP (AC3MP), Arducam 5MP (AC5MP), and OmniVision 5640 (OV5640). For reference, the cyan line is the power output of the signal from an aldosterone CLIA (sample conc. 100 pg mL −1 ). c Comparison of our (dark grey) and the smartphone’s native (light grey) app’s signal-to-noise ratio (SNR), signal, and noise ( n = 15 images). d Precision of signal quantification for signal (dark grey) and background (light grey) for the smartphones HP20 and X13P as the relative error (% of full-scale). e SNR as a function of image acquisition parameters using the X13P (ISO and exposure time (s)). f The effect of image processing algorithms on the SNR using the X13P with 17 OD 0.9 filters added above the LED, calculated for the raw signal (RAW, n = 225 average over 15 images for 15 sets), the ROF denoised signal (ROF, n = 225 average over 15 images for 15 sets), the NREA processed signal on RAW and ROF denoised images NREA ROF ( n = 15, each).
    Figure Legend Snippet: Camera Sensor Evaluation with MCCS. a Workflow using the MCCS to test configurations for CLIA testing. b Optical power limit for different cameras measured with a blue LED and varying numbers of neutral density filters (OD). Xiaomi 13 Pro (X13P), Huawei P60 Pro (HP60P), Huawei P20 (HP20), Fairphone 5 (F5), Nokia G22 (NG22), Arducam 3MP (AC3MP), Arducam 5MP (AC5MP), and OmniVision 5640 (OV5640). For reference, the cyan line is the power output of the signal from an aldosterone CLIA (sample conc. 100 pg mL −1 ). c Comparison of our (dark grey) and the smartphone’s native (light grey) app’s signal-to-noise ratio (SNR), signal, and noise ( n = 15 images). d Precision of signal quantification for signal (dark grey) and background (light grey) for the smartphones HP20 and X13P as the relative error (% of full-scale). e SNR as a function of image acquisition parameters using the X13P (ISO and exposure time (s)). f The effect of image processing algorithms on the SNR using the X13P with 17 OD 0.9 filters added above the LED, calculated for the raw signal (RAW, n = 225 average over 15 images for 15 sets), the ROF denoised signal (ROF, n = 225 average over 15 images for 15 sets), the NREA processed signal on RAW and ROF denoised images NREA ROF ( n = 15, each).

    Techniques Used: Comparison

    A method for rapid CLIA development and characterization using the MR device. a Schematic of the key steps and timings for the magnetic microparticle-based CLIA used in these studies. b 3D-rendering of custom-built magnetic microparticle transfer tool compatible with ANSI/SLAS Microplate Standard 96-well microplates or equivalent 8-well strips. c Focus control of our app, demonstrating out-of-focus and in-focus images using the MCCS. d Time-lapse capabilities of our app with fixed intervals in automatic mode (Automatic) or flexible interval defined by user (Manual) and resulting images acquired with MCCS. e Time dependence of signals for a renin (top) and aldosterone (bottom) standard curve with the start time of the signal reaching steady state (dashed line, on average 200 and 400 s for aldosterone and renin, respectively) and period during which the signal remains stable (grey box). f Intra-day (ID) and inter-day (InterD) measured on different days (D) with two technical replicates (R), assay signal and coefficient of variation (%CV) measured with the MR for five concentrations of a renin standard curve.
    Figure Legend Snippet: A method for rapid CLIA development and characterization using the MR device. a Schematic of the key steps and timings for the magnetic microparticle-based CLIA used in these studies. b 3D-rendering of custom-built magnetic microparticle transfer tool compatible with ANSI/SLAS Microplate Standard 96-well microplates or equivalent 8-well strips. c Focus control of our app, demonstrating out-of-focus and in-focus images using the MCCS. d Time-lapse capabilities of our app with fixed intervals in automatic mode (Automatic) or flexible interval defined by user (Manual) and resulting images acquired with MCCS. e Time dependence of signals for a renin (top) and aldosterone (bottom) standard curve with the start time of the signal reaching steady state (dashed line, on average 200 and 400 s for aldosterone and renin, respectively) and period during which the signal remains stable (grey box). f Intra-day (ID) and inter-day (InterD) measured on different days (D) with two technical replicates (R), assay signal and coefficient of variation (%CV) measured with the MR for five concentrations of a renin standard curve.

    Techniques Used: Control



    Similar Products

    modular camera calibration system mccs  (Autodesk Inc)
    86
    Autodesk Inc modular camera calibration system mccs
    Mobile platform for the evaluation and development of CLIAs. a Workflow of the development and evaluation platform, including the selection of an image sensor, its acquisition conditions, measuring standard curves, and optimization studies. b Schematic illustration of the Mobile Camera Characterization System <t>(MCCS)</t> for the in silico sensor and signal processing evaluation. c Schematic illustration of the Mobile Reader (MR) for CLIA signal quantification. d Mobile app to control the MCCS and MR. e Quantification control and analysis for a biomarker standard curve targeting Renin. Analyte concentrations, the resulting raw image, and control of well detection accuracy (yellow circle). A squared region of interest (ROI, yellow box) and background (BG) indicate some of the areas assessed for signal and background quantification, respectively. f Box plot of raw pixel values from ROI as a function of the biomarker concentration analyzed by primary image channel (red, green, blue) with pairwise statistical significance test. The box represents the inter-quartile range (IQR), with the median (hereafter termed signal) shown as a horizontal line. Whiskers extend to the most extreme data points within 1.5 times the IQR. n = 3721 pixels.
    Modular Camera Calibration System Mccs, supplied by Autodesk Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/modular camera calibration system mccs/product/Autodesk Inc
    Average 86 stars, based on 1 article reviews
    modular camera calibration system mccs - by Bioz Stars, 2026-05
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    Mobile platform for the evaluation and development of CLIAs. a Workflow of the development and evaluation platform, including the selection of an image sensor, its acquisition conditions, measuring standard curves, and optimization studies. b Schematic illustration of the Mobile Camera Characterization System (MCCS) for the in silico sensor and signal processing evaluation. c Schematic illustration of the Mobile Reader (MR) for CLIA signal quantification. d Mobile app to control the MCCS and MR. e Quantification control and analysis for a biomarker standard curve targeting Renin. Analyte concentrations, the resulting raw image, and control of well detection accuracy (yellow circle). A squared region of interest (ROI, yellow box) and background (BG) indicate some of the areas assessed for signal and background quantification, respectively. f Box plot of raw pixel values from ROI as a function of the biomarker concentration analyzed by primary image channel (red, green, blue) with pairwise statistical significance test. The box represents the inter-quartile range (IQR), with the median (hereafter termed signal) shown as a horizontal line. Whiskers extend to the most extreme data points within 1.5 times the IQR. n = 3721 pixels.

    Journal: medRxiv

    Article Title: CLIA MDK: A Modular Smartphone Platform Matching Plate Reader Performance for Chemiluminescent Immunoassay Development

    doi: 10.64898/2026.03.26.26348440

    Figure Lengend Snippet: Mobile platform for the evaluation and development of CLIAs. a Workflow of the development and evaluation platform, including the selection of an image sensor, its acquisition conditions, measuring standard curves, and optimization studies. b Schematic illustration of the Mobile Camera Characterization System (MCCS) for the in silico sensor and signal processing evaluation. c Schematic illustration of the Mobile Reader (MR) for CLIA signal quantification. d Mobile app to control the MCCS and MR. e Quantification control and analysis for a biomarker standard curve targeting Renin. Analyte concentrations, the resulting raw image, and control of well detection accuracy (yellow circle). A squared region of interest (ROI, yellow box) and background (BG) indicate some of the areas assessed for signal and background quantification, respectively. f Box plot of raw pixel values from ROI as a function of the biomarker concentration analyzed by primary image channel (red, green, blue) with pairwise statistical significance test. The box represents the inter-quartile range (IQR), with the median (hereafter termed signal) shown as a horizontal line. Whiskers extend to the most extreme data points within 1.5 times the IQR. n = 3721 pixels.

    Article Snippet: The modular camera calibration system (MCCS) and mobile reader (MR) were designed with Autodesk Inventor Professional (Autodesk, USA).

    Techniques: Selection, In Silico, Control, Biomarker Discovery, Concentration Assay

    Camera Sensor Evaluation with MCCS. a Workflow using the MCCS to test configurations for CLIA testing. b Optical power limit for different cameras measured with a blue LED and varying numbers of neutral density filters (OD). Xiaomi 13 Pro (X13P), Huawei P60 Pro (HP60P), Huawei P20 (HP20), Fairphone 5 (F5), Nokia G22 (NG22), Arducam 3MP (AC3MP), Arducam 5MP (AC5MP), and OmniVision 5640 (OV5640). For reference, the cyan line is the power output of the signal from an aldosterone CLIA (sample conc. 100 pg mL −1 ). c Comparison of our (dark grey) and the smartphone’s native (light grey) app’s signal-to-noise ratio (SNR), signal, and noise ( n = 15 images). d Precision of signal quantification for signal (dark grey) and background (light grey) for the smartphones HP20 and X13P as the relative error (% of full-scale). e SNR as a function of image acquisition parameters using the X13P (ISO and exposure time (s)). f The effect of image processing algorithms on the SNR using the X13P with 17 OD 0.9 filters added above the LED, calculated for the raw signal (RAW, n = 225 average over 15 images for 15 sets), the ROF denoised signal (ROF, n = 225 average over 15 images for 15 sets), the NREA processed signal on RAW and ROF denoised images NREA ROF ( n = 15, each).

    Journal: medRxiv

    Article Title: CLIA MDK: A Modular Smartphone Platform Matching Plate Reader Performance for Chemiluminescent Immunoassay Development

    doi: 10.64898/2026.03.26.26348440

    Figure Lengend Snippet: Camera Sensor Evaluation with MCCS. a Workflow using the MCCS to test configurations for CLIA testing. b Optical power limit for different cameras measured with a blue LED and varying numbers of neutral density filters (OD). Xiaomi 13 Pro (X13P), Huawei P60 Pro (HP60P), Huawei P20 (HP20), Fairphone 5 (F5), Nokia G22 (NG22), Arducam 3MP (AC3MP), Arducam 5MP (AC5MP), and OmniVision 5640 (OV5640). For reference, the cyan line is the power output of the signal from an aldosterone CLIA (sample conc. 100 pg mL −1 ). c Comparison of our (dark grey) and the smartphone’s native (light grey) app’s signal-to-noise ratio (SNR), signal, and noise ( n = 15 images). d Precision of signal quantification for signal (dark grey) and background (light grey) for the smartphones HP20 and X13P as the relative error (% of full-scale). e SNR as a function of image acquisition parameters using the X13P (ISO and exposure time (s)). f The effect of image processing algorithms on the SNR using the X13P with 17 OD 0.9 filters added above the LED, calculated for the raw signal (RAW, n = 225 average over 15 images for 15 sets), the ROF denoised signal (ROF, n = 225 average over 15 images for 15 sets), the NREA processed signal on RAW and ROF denoised images NREA ROF ( n = 15, each).

    Article Snippet: The modular camera calibration system (MCCS) and mobile reader (MR) were designed with Autodesk Inventor Professional (Autodesk, USA).

    Techniques: Comparison

    A method for rapid CLIA development and characterization using the MR device. a Schematic of the key steps and timings for the magnetic microparticle-based CLIA used in these studies. b 3D-rendering of custom-built magnetic microparticle transfer tool compatible with ANSI/SLAS Microplate Standard 96-well microplates or equivalent 8-well strips. c Focus control of our app, demonstrating out-of-focus and in-focus images using the MCCS. d Time-lapse capabilities of our app with fixed intervals in automatic mode (Automatic) or flexible interval defined by user (Manual) and resulting images acquired with MCCS. e Time dependence of signals for a renin (top) and aldosterone (bottom) standard curve with the start time of the signal reaching steady state (dashed line, on average 200 and 400 s for aldosterone and renin, respectively) and period during which the signal remains stable (grey box). f Intra-day (ID) and inter-day (InterD) measured on different days (D) with two technical replicates (R), assay signal and coefficient of variation (%CV) measured with the MR for five concentrations of a renin standard curve.

    Journal: medRxiv

    Article Title: CLIA MDK: A Modular Smartphone Platform Matching Plate Reader Performance for Chemiluminescent Immunoassay Development

    doi: 10.64898/2026.03.26.26348440

    Figure Lengend Snippet: A method for rapid CLIA development and characterization using the MR device. a Schematic of the key steps and timings for the magnetic microparticle-based CLIA used in these studies. b 3D-rendering of custom-built magnetic microparticle transfer tool compatible with ANSI/SLAS Microplate Standard 96-well microplates or equivalent 8-well strips. c Focus control of our app, demonstrating out-of-focus and in-focus images using the MCCS. d Time-lapse capabilities of our app with fixed intervals in automatic mode (Automatic) or flexible interval defined by user (Manual) and resulting images acquired with MCCS. e Time dependence of signals for a renin (top) and aldosterone (bottom) standard curve with the start time of the signal reaching steady state (dashed line, on average 200 and 400 s for aldosterone and renin, respectively) and period during which the signal remains stable (grey box). f Intra-day (ID) and inter-day (InterD) measured on different days (D) with two technical replicates (R), assay signal and coefficient of variation (%CV) measured with the MR for five concentrations of a renin standard curve.

    Article Snippet: The modular camera calibration system (MCCS) and mobile reader (MR) were designed with Autodesk Inventor Professional (Autodesk, USA).

    Techniques: Control